Exploring Machine Learning Approaches for Future Disease Prediction in Gynecology: An Experimental Study

This study delves into the potential of machine learning in the early detection of cervical cancer, a condition that remains one of the most significant health burdens in resource-limited settings, where delays and inaccuracies in diagnostics frequently lead to adverse outcomes. Utilizing a dataset of 858 patients treated at the Universitario de Caracas Hospital, the research evaluates various supervised learning models, including Support Vector Machines, Decision Trees, and Random Forests, to predict risk factor classifications in cervical cancer. The methodology incorporates data preprocessing, feature selection, and cross-validation using the caret package to ensure the results are robust, reliable, and reproducible. Among the models tested, Random Forest demonstrated superior performance, achieving the highest scores in accuracy, precision, and recall, making it the most effective tool in this study. Critical risk factors such as age, lifestyle, and medical history were highlighted through visual tools like feature importance heat maps and confusion matrices, emphasizing their role in predictive accuracy. This research underscores the pressing need for innovative technological solutions that assist healthcare professionals in diagnostic and treatment planning processes, particularly in under-resourced areas. By leveraging machine learning, these diagnostic gaps can be bridged, enabling timely interventions and significantly improving patient outcomes. Moreover, the findings illuminate the broader transformative potential of machine learning, not only in gynecological health but also in expanding predictive diagnostics to other medical domains, thereby revolutionizing healthcare systems on a global scale.